code: def register_output_backward_hook(self):
      for ts in self._target_spaces.values():
          is_output = any(
              [
                  target_space.type is TargetType.OUTPUT
                  for target_space in ts.values()
              ]
          )
          is_param = any(
              [
                  target_space.type is TargetType.PARAMETER
                  for target_space in ts.values()
              ]
          )
          if is_param and not is_output:
              if is_proper_torch_version:  # torch version >= 2.0.0
                  for target_space in ts.values():
                      if target_space.type is TargetType.PARAMETER:
                          self._quant_dequant_gradient_hook(
                              target_space
                          )
                          break
              else:
                  warn_msg = f"Gradient quantization is only supported for torch version >= 2.0.0"
                  _logger.warning(warn_msg)
  
imports: from __future__ import annotations
  import logging
  from typing import List, Dict, Union, overload
  import torch
  import torch.nn
  from torch import Tensor
  from nni.common.version import torch_version_is_2
  from ..base.compressor import Compressor, Quantizer
  from ..base.wrapper import ModuleWrapper
  from ..utils import Evaluator, _EVALUATOR_DOCSTRING
  from ..base.target_space import TargetType, QuantizationTargetSpace
label_desc: Use image node_id 7 for calling the DoReFaQuantizer obj's underlying member method code with example usage: obj.register_output_backward_hook() without return types